00_loadData.R

Read in data from Sky Pond (alpine) and The Loch (subalpine)

Sky littoral sites

SB1 <- read_csv(here("data/SB1_2016.csv")) %>%
  mutate(dateTime = mdy_hm(dateTime))
SB2 <- read_csv(here("data/SB2_2016.csv")) %>%
  mutate(dateTime = mdy_hm(dateTime))
SB3 <- read_csv(here("data/SB3_2016.csv")) %>%
  mutate(dateTime = mdy_hm(dateTime))
SB4 <- read_csv(here("data/SB4_2016.csv")) %>%
  mutate(dateTime = mdy_hm(dateTime))
SB5 <- read_csv(here("data/SB5_2016.csv")) %>%
  mutate(dateTime = mdy_hm(dateTime))

Loch littoral sites

LB1 <- read_csv(here("data/LochInlet_2016.csv")) %>%
  mutate(dateTime = mdy_hm(dateTime))
LB3 <- read_csv(here("data/LB3_2016.csv")) %>%
  mutate(dateTime = mdy_hm(dateTime))
LB4 <- read_csv(here("data/LB4_2016.csv")) %>%
  mutate(dateTime = mdy_hm(dateTime))
LB5 <- read_csv(here("data/LB5_2016.csv")) %>%
  mutate(dateTime = mdy_hm(dateTime))
LB6 <- read_csv(here("data/LB6_2016.csv")) %>%
  mutate(dateTime = mdy_hm(dateTime))

Master littoral

sky_littoral <- bind_rows(SB1, SB2, SB3, SB4, SB5) %>%
  mutate(habitat = "littoral")
loch_littoral <- bind_rows(LB3, LB4, LB5, LB6, LB1) %>%
  mutate(habitat = "littoral")
littoral_master <- bind_rows(sky_littoral,
                             loch_littoral) %>%
  mutate(dateTime = round_date(dateTime, "hour")) #For making joining to pelagic data easier

Sky buoy temperature

sky_buoy_long <-
  read.table(here("data/sky_2016_tempProfile.txt"),
             sep = ",",
             header = TRUE) %>%
  mutate(
    dateTime = ymd_hms(as.factor(dateTime)),
    dateTime = force_tz(dateTime, tz = "America/Denver"),
    dateTime = with_tz(dateTime, "GMT")
  ) %>%
  filter(dateTime >= "2016-06-13" &
           dateTime <= "2016-10-30") %>% #ice off and on dates
  rename(wtr_6.5 = wtr_7.0) %>%
  pivot_longer(-dateTime, names_to = "depth") %>%
  mutate(habitat = "pelagic") %>%
  separate(col = depth,
           into = c("parameter", "depth"),
           sep = "_") %>%
  mutate(parameter = "temperature",
         lakeID = "SkyPond")

• Note that 0.5 and 6.5 are MiniDOT observations which are more frequent than the others
• All other depths were recorded with iButtons, same as the littoral zone sites
• sky_2016_sensorTemp_6.5m.txt is the temperature only at 6.5m; MiniDOT
• sky_2016_sensorTemp.txt is the temperature only at 0.5m; MiniDOT # Loch buoy temperature

loch_buoy_long <- read.table(here("data/loch_2016_tempProfile.txt"), sep=",", header=TRUE) %>%
  mutate(dateTime = ymd_hms(as.factor(dateTime)),
         dateTime = force_tz(dateTime, tz="America/Denver"),
         dateTime = with_tz(dateTime, "GMT")) %>%
  filter(dateTime > "2016-05-31" & dateTime <= "2016-10-30") %>% #ice off and on dates
  pivot_longer(-dateTime, names_to="depth") %>%
  mutate(habitat="pelagic") %>%
  separate(col = depth, into = c("parameter", "depth"), sep = "_") %>%
  mutate(parameter="temperature",
         lakeID="TheLoch")

• Note that 0.5 and 4.5 are MiniDOT observations which are more frequent than the others
• All other depths were recorded with iButtons, same as the littoral zone sites
• loch_2016_sensorTemp_4.5m.txt is the temperature only at 4.5; MiniDOT
• loch_2016_sensorTemp_0.5m.txt is the temperature only at 0.5m; MiniDOT # Sky buoy DO

sky_DO_0.5 <-
  read.table(here("data/sky_2016_DO_0.5m.txt"),
             sep = ",",
             header = TRUE) %>%
  mutate(
    dateTime = ymd_hms(dateTime),
    depth = 0.5,
    lakeID = "SkyPond",
    habitat = "pelagic"
  ) %>%
  filter(dateTime < "2016-11-02")

sky_DO_6.5 <-
  read.table(here("data/sky_2016_DO_6.5m.txt"),
             sep = ",",
             header = TRUE) %>%
  mutate(
    dateTime = ymd_hms(dateTime),
    depth = 6.5,
    lakeID = "SkyPond",
    habitat = "pelagic"
  ) %>%
  filter(dateTime < "2016-11-02")

Loch buoy DO

loch_DO_0.5 <-
  read.table(here("data/loch_2016_DO_0.5m.txt"),
             sep = ",",
             header = TRUE) %>%
  mutate(
    dateTime = ymd_hms(dateTime),
    depth = 0.5,
    lakeID = "TheLoch",
    habitat = "pelagic"
  )
loch_DO_4.5 <-
  read.table(here("data/loch_2016_DO_4.5m.txt"),
             sep = ",",
             header = TRUE) %>%
  mutate(
    dateTime = ymd_hms(dateTime),
    depth = 4.5,
    lakeID = "TheLoch",
    habitat = "pelagic"
  ) 

Combine all DO data

DO_master <- bind_rows(sky_DO_0.5,
                       sky_DO_6.5,
                       loch_DO_0.5,
                       loch_DO_4.5) %>%
  rename(value=DO) %>%
  mutate(parameter="DO",
         depth=as.character(as.numeric(depth)))

Master buoy df

buoy_master <-
  bind_rows(DO_master, loch_buoy_long, sky_buoy_long) %>%
  mutate(
    depth_category = case_when(depth == 0.5 ~ "surface",
                               TRUE ~ "bottom"),
    depth_category = factor(depth_category,
                            levels = c("surface", "bottom")),
    season = case_when(dateTime > "2016-09-01" ~ "fall",
                       TRUE                      ~ "summer"),
    season = factor(season,
                    levels = c("summer", "fall"))
  ) 

Data vis: D.O. from buoys —————————————————————-

All DO, line graph

Plotting just summer below, where we have overlap with littoral zone measurements

How much does DO fluctuate daily at each depth?

Data vis: temperature from buoys —————————————————————-

All temps,line graph

Just summer when all sensors were present

How much does temperature fluctuate daily at each depth?

Data vis: temperature from littoral zone —————————————————————-

Littoral zones temperature in both lakes, 2016

How large are the diurnal swings?

Histograms of diel temperature swings

How do the littoral zone temperatures compared to 0.5m temperatures? Separate panel for each site

How do the littoral zone temperatures compared to 0.5m temperatures? Include 0.5m depth in the background of each panel

How do the littoral zone diel fluctuations compare to 0.5m temperatures? Include 0.5m depth in the background of each panel